How to Learn Big Data: Most Important Factors to Consider

The birth of transistors revolutionized the chip industry. Along with transistors came the capability to increase its growth and that growth doubled in every 18 months, as Gordon Moore had correctly observed back in the 1960s. These transistors vastly increased the processing power and computing speed of the devices and continue to do so. With this comes the capacity to access, store and handle a large amount of data. Back to what Moore’s law stated, the data is also increasing in that rate. And, today we see a massive amount of data around us and it’s huge to the point that it is almost difficult to extract the useful information. But, such ubiquitous data is critical to the enterprises and organizations. Talking about the volume of data, also referred to as “Big Data”, most of that is unstructured and enterprises encounter mostly unstructured, complex data. Sources of these unstructured data are social media sites, images, audio & video files, scientific data etc.

But, the most important question is what are we doing with such voluminous amount of data? As an enterprise, are we extracting the right information and using them to our advantage? Are we harnessing the real power of big data for our competitive, business decisions? Whether you are a startup or an established organization or an enthusiastic learner for that matter, what are the essential things you must consider before or while learning about big data?

Here are the few key ingredients:

Mindset: Someone who is keen on taking the journey in Data Science must have an analytical bent of mind. There must be a sense of curiosity to dig deeper and should possess a scientific temperament towards researching, collecting, analyzing and identifying data inputs. A mindset to understand the consumer behavior and trends and assess the patterns. Based on those studies, the enterprises would be able to take potential decisions.

Enthusiastic learner: Even if you don’t hold a degree in computer science or you aren’t a data scientist, your pursuit of learning big data or data science is very important. If you are passionate about learning, then you can avail yourself of resources available online like Coursera, edx or take training courses from Cloudera or Big Data University, etc. Basically, if you are enthusiastic towards a Data Science career then, you are not limited by your lack of degree.

Cool Platforms: Right from providing tutorials to hosting data science competitions, Kaggle is a haven for data scientists and those budding ones. It is the largest community of data scientists. A platform like Kaggle offers complex business problems to be worked upon and in the process you can enhance your coding skills and knowledge.

Knowledge: When it comes to processing large data sets, Python is an effective and essential scripting language to learn. For statistics, R is a good programming language. The important thing is to first learn about the fundamentals like algorithms and data structures. Python comes out as a winner because its code is shorter and easier to learn and the syntax is easy. Depending on profiles, different skills are required. For data visualization, R is an effective language and there are Scala, Java that a Data engineer is expected to know. Basically, one shouldn’t limit himself to learning a particular language. The field of learning is wide open and always progressive.

So, jump on the bandwagon, hone your skills, and carve out a unique, passionate career for yourself.